Improvement of drilling project efficiency: AI-based roadheader performance prediction and evaluation

The economic assessment of a drilling project heavily relies on forecasting the drilling machine performance. The net cutting rate of a roadheader is a crucial parameter for performance evaluation, and it can be estimated using various methods. In this study, various models were developed to predict roadheader performance and their accuracy and desirability were compared. The models utilized artificial intelligence techniques such as support vector machine (SVM), artificial neural network (ANN), K-fold nearest neighbor (KNN), random forest (RF), and linear regression (LR). The input variables for these models were rock quality designation (RQD) and the return values of the Schmidt hammer R1, R2, and R3. The modeling task was conducted using the Orange data mining software.